API Data Preprocessing

This project demonstrates how to collect real time data from APIs and transform complex JSON responses into structured datasets suitable for machine learning and data analysis workflows.

The notebook includes data fetching, preprocessing, JSON normalization, dataset generation, and CSV export using Python and Pandas.

Features

API based data collection

Nested JSON processing

Data cleaning and preprocessing

Conversion of raw API responses into structured tabular datasets

Dataset export for machine learning workflows

Technologies Used

Python

Pandas

Requests

Google Colab

Project Workflow

Fetch data from an external API

Convert raw JSON responses into Python objects

Normalize and preprocess nested data structures

Generate clean tabular datasets

Export datasets in CSV format for further analysis and machine learning tasks

Files

apifetching.ipynb

dataset.csv

Purpose

The purpose of this project is to demonstrate practical data engineering and preprocessing techniques commonly used in machine learning pipelines and real world data driven applications.